Senior Applied Machine Learning Engineer

Hewlett Packard Enterprise•Durham, NC

6d•Hybrid

About The Position

Hewlett Packard Enterprise is seeking an experienced Senior Applied Machine Learning Engineer to build, optimize, and integrate machine learning and AI solutions for customer needs and product performance. The role emphasizes hands-on application of AI/ML in real-world, customer-facing or end-user environments, translating complex models and data into accessible, scalable solutions and supporting successful deployment and adoption in collaboration with cross-functional teams. The position is hybrid, requiring an average of two days per week in an HPE office. The expected annual base salary range in Georgia and North Carolina is USD 141,000–325,000. Variable incentives may also be offered. HPE describes benefits supporting physical, financial, and emotional wellbeing, along with career development programs and an inclusive workplace.

Requirements

PhD with at least 2 years of relevant industry experience, or equivalent experience such as a Master’s degree with 4 or more years or a Bachelor’s degree with 6 or more years.
Extensive hands-on experience applying machine learning and AI solutions in customer-facing or end-user environments.
Proven ability to deploy models in production while ensuring reliability and performance.
Experience with open-source ML/AI tools and frameworks.
Experience with backend programming languages such as Python or Go.
Proficiency in developing, using, and maintaining AI agents; proven experience coding agents for automation or decision-making tasks.
Excellent written and verbal communication skills, especially in asynchronous collaboration.

Nice To Haves

Knowledge of inference deployment and optimization techniques.
Familiarity with large-scale data pipelines.
Experience with retrieval-augmented systems such as RAG.
Experience with AI agents and automation.

Responsibilities

Design, develop, and deploy machine learning models and AI solutions for real-world customer problems, prioritizing usability, scalability, and performance.
Rapidly develop demos, proofs of concept, minimum viable products, and workflows to showcase AI/ML capabilities and improve product features based on customer feedback or market research.
Experiment with emerging techniques and tools to produce functional prototypes for practical problems.
Develop and improve production integrations of open-source ML/AI tools, including MLFlow, Spark, LangChain, and Kubeflow, ensuring operation on platforms such as Kubernetes.
Fine-tune models and algorithms, including deep learning technologies, for accuracy, efficiency, and scalability in production.
Translate customer requirements and industry trends into AI/ML solutions that improve product features, data management, and system performance.
Collaborate with product managers, data scientists, and engineering teams to design and deploy solutions, and document procedures and best practices.
Lead integration of emerging AI tools, mentor junior team members, and communicate progress and challenges to leadership.
Develop, use, and maintain AI agents, including agents for automation or decision-making tasks.